The subject matter disclosed herein relates generally to imaging systems, and more particularly, to a method and apparatus for reconstructing an image using iterative techniques.
Traditionally, images have been reconstructed from Computed Tomography (CT) data using direct reconstruction algorithms such as filtered back projection (FBP) or convolution back projection (CBP). Recently, model based iterative reconstruction (MBIR) algorithms have become commercially available for reconstructing CT images. One advantage of MBIR algorithms is that MBIR algorithms can more accurately model the measurements obtained from CT systems. This is particularly true for helical CT systems with multi-slice detectors because helical CT systems produce projection measurements that pass obliquely through the two-dimensional (2D) reconstructed image planes, and the acquired data is inherently noisy. By more accurately modeling these projections, MBIR algorithms can produce reconstructions with higher quality (e.g., resolution), lower noise, and fewer artifacts. As a result, MBIR algorithms may be used as a tool to significantly reduce the dose in CT scans while maintaining the diagnostic image quality.
However, a major challenge of MBIR is the computation time and computational resources required to complete a reconstruction. MBIR algorithms typically reconstruct an image by first forming an objective function that incorporates an accurate system model, statistical noise model, and prior model. With the objective function in hand, the image is then reconstructed by computing an estimate that minimizes the objective function, which is typically performed using an iterative optimization algorithm. Examples of some of such iterative optimization algorithms include iterative coordinate descent (ICD), variations of expectation maximization (EM), conjugate gradients (CG), and ordered subsets (OS). However, because of the complexity of the MBIR objective function and the associated iterative solution, some iterative algorithms may require a relatively high number of iterations to achieve the final estimate. As a result, known iterative algorithms that solve the MBIR objective function may require a relatively large amount of time to reconstruct an image.